Driving Performance and Preferences for Motion Cueing Algorithms of Amateurs and Professional Racing Drivers

Abstract In professional motorsport, driving simulators play an important role in driver and racing team training, as well as race car development and testing. Their usability relies on cueing systems, with the Motion Cueing Algorithm (MCA) being responsible for translating virtual vehicle motion into simulator motion demands. This study evaluates the effects of MCAs on 31 amateurs and two professional racing drivers in a four-degrees-of-freedom driving simulator. Assessment criteria are based on drivers’ performance metrics (e.g., lap time, probability of fatal errors), objective driving characteristics (e.g., steering wheel reversal rate, full-throttle ratio), subjective workload, and preferences for specific MCAs. The results revealed consistent MCA preferences for both professional racing drivers, which are supported by their superior driving performance. In contrast, preferences among amateur drivers were distributed across the MCAs, and their subjective workload was at least double that of the professionals. The absence of a common MCA preference in the amateur group may be attributed to their high workload, combined with limited experience and inappropriate mental models for race driving. These results are important because they suggest that motorsport driving simulators, even if used for less experienced or non-experienced racing drivers, should rely on the MCAs preferred by professional racing drivers. The findings further underscore the importance of MCA tuning by professionals with an adequate reference to the real world scenario that is to be tested in the virtual environment.